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import torchinfo


def get_model_complexity_info(model, input_size, *args, **kwargs):
    input_res = (3, *input_size) if isinstance(input_size, (list, tuple)) else \
        (3, input_size, input_size)
    model.eval()
    input_res = input_res if isinstance(input_res, (list,tuple)) else (3, input_res, input_res)
    data_shape = (1,*input_res)
    # here next() is same as using list()[0], but more efficient,
    # since it doesn't convert the whole generator to list
    device = next(model.parameters()).device
    forward_backup = model.forward
    model.forward = model.forward_dummy
    try:
        torchinfo.summary(model, data_shape, depth=10, device=device)
    except UnicodeEncodeError:
        pass
    #
    model.forward = forward_backup
